Neural Forecasting of the Day-Ahead Hourly Power Curve of a Photovoltaic Plant

被引：0

作者：

Ogliari, Emanuele ^{[1
]}

Gandelli, Alessandro ^{[1
]}

Grimaccia, Francesco ^{[1
]}

Leva, Sonia ^{[1
]}

Mussetta, Marco ^{[1
]}

机构：

[1] Politecn Milan, Dipartimento Energia, Via La Masa 34, I-20156 Milan, Italy

来源：

2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN) | 2016年

关键词：

MODEL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper we propose a study to identify the best ANN configuration in terms of number of neurons, number of layers, training-set size, in order to perform the day-ahead energy production forecast for a Photo-Voltaic (PV) plant. This set up is applied to a novel hybrid method (PHANN Physic Hybrid Artificial Neural Network) in order to enhance the energy dayahead forecast combining both the deterministic Clear Sky Solar Radiation Algorithm (CSRM) and the stochastic ANN method. This hybrid method has been tested on different PV plants in Italy. Finally, different weather conditions have been taken into account to test the robustness of the algorithm as well as the effects of particular events on the forecasting output.

引用

页码：654 / 659

页数：6

共 50 条

[31] A Simplified LSTM Neural Networks for One Day-Ahead Solar Power Forecasting
Liu, Chun-Hung
Gu, Jyh-Cherng
Yang, Ming-Ta
[J]. IEEE ACCESS, 2021, 9 : 17174 - 17195
[32] Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
Nespoli, Alfredo
Ogliari, Emanuele
Leva, Sonia
Pavan, Alessandro Massi
Mellit, Adel
Lughi, Vanni
Dolara, Alberto
[J]. ENERGIES, 2019, 12 (09)
[33] Industry Experience of Developing Day-Ahead Photovoltaic Plant Forecasting System Based on Machine Learning
Khalyasmaa, Alexandra I.
Eroshenko, Stanislav A.
Tashchilin, Valeriy A.
Ramachandran, Hariprakash
Piepur Chakravarthi, Teja
Butusov, Denis N.
[J]. REMOTE SENSING, 2020, 12 (20) : 1 - 21
[34] Distributed Reconciliation in Day-Ahead Wind Power Forecasting
Bai, Li
Pinson, Pierre
[J]. ENERGIES, 2019, 12 (06)
[35] The value of day-ahead solar power forecasting improvement
Martinez-Anido, Carlo Brancucci
Botor, Benjamin
Florita, Anthony R.
Draxl, Caroline
Lu, Siyuan
Hamann, Hendrik F.
Hodge, Bri-Mathias
[J]. SOLAR ENERGY, 2016, 129 : 192 - 203
[36] Benefit Cost Analysis of Improved Forecasting for Day-Ahead Hourly Regulation Requirements
Weimar, Mark
Somani, Abhishek
Etingov, Pavel
Miller, Laurie
Makarov, Yuri
Loutan, Clyde
Katzenstein, Warren
[J]. 2018 IEEE POWER & ENERGY SOCIETY GENERAL MEETING (PESGM), 2018,
[37] Univariate forecasting of day-ahead hourly electricity demand in the northern grid of India
Ghosh, Sajal
[J]. INTERNATIONAL JOURNAL OF INDIAN CULTURE AND BUSINESS MANAGEMENT, 2009, 2 (06) : 625 - 637
[38] Evaluating the Application of Energy Storage and Day-Ahead Solar Forecasting to Firm the Output of a Photovoltaic Plant
Kreikebaum, Frank
Moghe, Rohit
Prasai, Anish
Divan, Deepak
[J]. 2011 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE), 2011, : 3556 - 3561
[39] Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory
Yu, Dukhwan
Choi, Wonik
Kim, Myoungsoo
Liu, Ling
[J]. ENERGIES, 2020, 13 (15)
[40] A Novel Approach for Day-Ahead Hourly Building-Integrated Photovoltaic Power Prediction by Using Feature Engineering and Simple Weather Forecasting Service
Jeong, Jinhwa
Lee, Dongkyu
Chae, Young Tae
[J]. ENERGIES, 2023, 16 (22)

← 1 2 3 4 5 →